mlsanityData to 7 Oct 2026

A Survey on Conversational Recommender Systems

arXiv 2004.00646 ↗First posted 1 Apr 2020cs.HC cs.AI cs.IRcited by 142

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Cited by 23 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

Key facts. Cited by 142 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 17; 2023: 28; 2024: 26; 2025: 39; 2026: 26.

142 papers.

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  1. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
  2. 2024From Persona to Personalization: A Survey on Role-Playing Language Agentscited by 206
  3. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  4. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  5. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 89
  6. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 72
  7. 2023Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferencescited by 66
  8. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  9. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  10. 2025A Survey on LLM-powered Agents for Recommender Systemscited by 49
  11. 2023Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Modelscited by 48
  12. 2024A Multi-Agent Conversational Recommender Systemcited by 42
38 more
  1. 2022A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systemscited by 32
  2. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 31
  3. 2022User-Centric Conversational Recommendation with Multi-Aspect User Modelingcited by 29
  4. 2025Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistantcited by 26
  5. 2024Large Language Models as Recommender Systems: A Study of Popularity Biascited by 25
  6. 2024Unleashing the Power of Continual Learning on Non-Centralized Devices: A Surveycited by 23
  7. 2023ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactionscited by 22
  8. 2024A Survey of Conversational Searchcited by 22
  9. 2023MuseChat: A Conversational Music Recommendation System for Videoscited by 21
  10. 2024Retrieval-Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Trackingcited by 18
  11. 2025Are Generative AI Agents Effective Personalized Financial Advisors?cited by 18
  12. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  13. 2023Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialoguecited by 15
  14. 2021KECRS: Towards Knowledge-Enriched Conversational Recommendation Systemcited by 14
  15. 2023Sparks of Artificial General Recommender (AGR): Early Experiments with ChatGPTcited by 13
  16. 2024Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seekingcited by 13
  17. 2023U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendationcited by 12
  18. 2025RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systemscited by 11
  19. 2021Generating Usage-related Questions for Preference Elicitation in Conversational Recommender Systemscited by 10
  20. 2023Semantic Parsing for Conversational Question Answering over Knowledge Graphscited by 10
  21. 2023Improving Conversational Recommendation Systems via Counterfactual Data Simulationcited by 10
  22. 2025Graph Retrieval-Augmented LLM for Conversational Recommendation Systemscited by 10
  23. 2022Conversational Recommendation: A Grand AI Challengecited by 9
  24. 2022Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systemscited by 9
  25. 2023Talk the Walk: Synthetic Data Generation for Conversational Music Recommendationcited by 9
  26. 2023Evaluating Task-oriented Dialogue Systems: A Systematic Review of Measures, Constructs and their Operationalisationscited by 9
  27. 2024Parameter-Efficient Conversational Recommender System as a Language Processing Taskcited by 9
  28. 2024Concept -- An Evaluation Protocol on Conversational Recommender Systems with System-centric and User-centric Factorscited by 9
  29. 2025Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decompositioncited by 9
  30. 2021Recommender systems based on graph embedding techniques: A comprehensive reviewcited by 8
  31. 2022Follow Me: Conversation Planning for Target-driven Recommendation Dialogue Systemscited by 8
  32. 2024Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systemscited by 8
  33. 2024Beyond Retrieval: Generating Narratives in Conversational Recommender Systemscited by 8
  34. 2024Beyond Persuasion: Towards Conversational Recommender System with Credible Explanationscited by 7
  35. 2021Machine Learning for Utility Prediction in Argument-Based Computational Persuasioncited by 6
  36. 2022Aligning Recommendation and Conversation via Dual Imitationcited by 6
  37. 2022Evaluation of Synthetic Datasets for Conversational Recommender Systemscited by 6
  38. 2023Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Datasetcited by 6

50 most cited of 142 All 142 (JSON)